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- W4253141219 abstract "F1exible job shop scheduling problem (FJSP) is an extension of job shop scheduling problem (JSP) that has received increasing attention in recent decades. FJSP is a high-dimensional combinatorial optimization problem. Using accurate algorithms to solve them is a challenge and costly. The difference is that a meta-heuristic algorithm is an algorithm based on intuition or experience that gives a feasible solution to the problem at an acceptable cost (referring to calculation time and space). Particle Swarm optimization (PSO) is a classic meta-heuristic algorithm that has achieved many successful applications. However, it is easy to converge prematurely when solving high-dimensional problems. Competitive Swarm optimizer (CSO), as a variant of particle swarm optimization, has excellent global search capabilities to deal with high-dimensional problems. Therefore, this article uses CSO to solve FJSP. We introduced five other algorithms as a comparison to verify our algorithm. Numerical comparison results show that CSO can optimize all FJSP better overall." @default.
- W4253141219 created "2022-05-12" @default.
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- W4253141219 date "2021-10-29" @default.
- W4253141219 modified "2023-09-26" @default.
- W4253141219 title "Competitive swarm optimizer for Solving Flexible Jobshop Scheduling Problem" @default.
- W4253141219 doi "https://doi.org/10.1109/acait53529.2021.9731219" @default.
- W4253141219 hasPublicationYear "2021" @default.
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